SoroushVahidi/scidocs huggingface dataset:
Impossibility of Distributed Consensus with One Faulty Process
(1985)

The consensus problem involves an asynchronous system of processes, some of which may be unreliable. The problem is for the reliable processes to agree on a binary value. In this paper, it is shown that every protocol for this problem has the possibility of nontermination, even with only one faulty process. By way of contrast, solutions are known for the synchronous case, the “Byzantine Generals” problem.
Estimating the relationship between isoseismal area and earthquake magnitude by a hybrid fuzzy-neural-network method
(1999)

Utilizing information diffusion method and artificial neural networks, we propose in this paper a hybrid fuzzy neural network to estimate the relationship between isoseismal area and earthquake magnitude. We focus on the study of incompleteness and contradictory nature of patterns in scanty historical earthquake records. Information diffusion method is employed to construct fuzzy relationships which are equal to the number of observations. Integration of the relationships can change the contradictory patterns into more compatible ones which, in turn, can smoothly and quickly train the feedforward neural network with backpropagation algorithm (BP) to obtain the final relationship. A practical application is employed to show the superiority of the model. @ 1999 Elsevier Science B.V. All rights reserved.
Mining significant graph patterns by leap search
(2008)

With ever-increasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with user-specified objective functions. Most objective functions are not antimonotonic, which could fail all of frequency-centric graph mining algorithms. In this paper, we give the first comprehensive study on general mining method aiming to find most significant patterns directly. Our new mining framework, called LEAP (Descending Leap Mine), is developed to exploit the correlation between structural similarity and significance similarity in a way that the most significant pattern could be identified quickly by searching dissimilar graph patterns. Two novel concepts, structural leap search and frequency descending mining, are proposed to support leap search in graph pattern space. Our new mining method revealed that the widely adopted branch-and-bound search in data mining literature is indeed not the best, thus sketching a new picture on scalable graph pattern discovery. Empirical results show that LEAP achieves orders of magnitude speedup in comparison with the state-of-the-art method. Furthermore, graph classifiers built on mined patterns outperform the up-to-date graph kernel method in terms of efficiency and accuracy, demonstrating the high promise of such patterns.
Mapping the lateral extent of human cadaver decomposition with soil chemistry.
(2012)

Soil below decomposing cadavers may have a different lateral spatial extent depending upon whether scavengers have access to the human cadaver or not. We examined the lateral spatial extent of decomposition products to a depth of 7cm of soils beneath two decomposing corpses, one in which the subject was autopsied, unclothed and placed under a wire cage to restrict scavenger access and one in which the subject was not autopsied, unclothed and exposed to scavengers. The two bodies had accumulated degree days (ADD) of 5799 and 5469 and post mortem interval (PMI) of 288 and 248d, respectively. The spatial extent for dissolved organic carbon (DOC) and organic nitrogen (DON) for both bodies was large but similar suggesting some movement off site for both compounds. Mean DOC was 1087±727 and 1484±1236μgg(-1) dry soil under the two corpses relative to 150±68μgg(-1) in upslope control soils. Sulfate tended to have 'hot spots' of lower values relative to the control soils indicative of anaerobic respiration. pH was lower and electrical conductivity was higher in the soil under both decomposing cadavers relative to control soils. Some of the nutrients examined downslope of the human remains were significantly higher than control soils upslope suggesting movement of decomposition products off-site which could be an important factor when using human remains detector dogs.
Text Steganography by Changing Words Spelling
(2008)

One of the important issues in security fields is hidden exchange of information. There are different methods for this purpose such as cryptography and steganography. Steganography is a method of hiding data within a cover media so that other individuals fail to realize their existence. In this paper a new method for steganography in English texts is proposed. In this method the US and UK spellings of words substituted in order to hide data in an English text. For example "color" has different spelling in UK (colour) and US (color). Therefore the data can be hidden in the text by substituting these words.
Analysis and Design of Tag Antenna Based UHF RFID for Libraries
(2015)

A kind of UHF RFID tag antenna for library management is designed. The series of design requirements are proposed through rich analytics. Refer to the management model of intelligent library based on UHF RFID. For the antenna, the size is mm mm 3 90  , adopting T-shaped matching which good for adjusting of antenna impedance matching, the center frequency by simulations is 918 MHz under application environment, Return Loss is less than -10dB in the frequency band of 860MHz~960MHz , the minimum value of return loss is -24.4dB . Simulation results show that the tag antenna has a good performance under application environment for library.
Antibiotic resistance is ancient
(2011)

The discovery of antibiotics more than 70 years ago initiated a period of drug innovation and implementation in human and animal health and agriculture. These discoveries were tempered in all cases by the emergence of resistant microbes. This history has been interpreted to mean that antibiotic resistance in pathogenic bacteria is a modern phenomenon; this view is reinforced by the fact that collections of microbes that predate the antibiotic era are highly susceptible to antibiotics. Here we report targeted metagenomic analyses of rigorously authenticated ancient DNA from 30,000-year-old Beringian permafrost sediments and the identification of a highly diverse collection of genes encoding resistance to β-lactam, tetracycline and glycopeptide antibiotics. Structure and function studies on the complete vancomycin resistance element VanA confirmed its similarity to modern variants. These results show conclusively that antibiotic resistance is a natural phenomenon that predates the modern selective pressure of clinical antibiotic use.
Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network
(2018)

Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal information into CNNs are still being actively explored in the recent literature. Motivated by the popular recurrent attention models in the research area of natural language processing, we propose the Attention-aware Temporal Weighted CNN (ATW CNN) for action recognition in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is simply implemented as temporal weighting yet it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Our experimental results on the UCF-101 and HMDB-51 datasets showed that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.
Comparison of complex network analysis software: Citespace, SCI2 and Gephi
(2017)

Big Data Analysis (BDA) has attracted considerable interest and curiosity from scientists of various fields recently. As big size and complexity of big data, it is pivotal to uncover hidden patterns, bursts of activity, correlations and laws of it. Complex network analysis could be effective method for this purpose, because of its powerful data organization and visualization ability. Besides the general complex software (Gephi), some bibliometrics software (Citespace and SCI2) could also be used for this analysis, due to their powerful data process and visualization functions. This paper presents a comparison of Citespace, SCI2 and Gephi from the following three aspects: (1) Data preprocessing. Citespace is time-consuming and laborious on merging synonyms. SCI2 is efficient and suitable for massive data. Both of them could remove duplicate records. However, Gephi lacks these. (2) Network extraction. Citespace pays more attention on temporal analysis, but SCI2 and Gephi pay less attention on it. Besides, Citespace and SCI2 could provide pruning algorithms highlight the main structure of social network, but Gephi lacks this. (3) Visualization. In Citespace, co-occurrence network could present time, frequency and betweenness centrality simultaneously; cluster view could label the clusters with phrases. However, SCI2 and Gephi provide more various layout algorithms to present network. Besides, they have a better edit-ability than Citespace on generated network.
Optimum Design of Traveling-Wave SIW Slot Array Antennas
(2013)

This paper presents an optimum design method based on the Method of Least Squares (MLS) for the traveling-wave substrate integrated waveguide (SIW) slot array antenna. Elliot's design formulas for dielectric filled waveguide including external and internal mutual coupling are used for the design by means of the equivalent waveguide. In order to achieve an efficient antenna, design formulas are extended to nonuniformly spaced slots. An error function consisting of four terms is formulated and then minimized with respect to the design parameters namely lengths, offsets, spacings and excitations of slots using combination of genetic algorithm and conjugate gradient method (GA-CG). A linear slot array at $X$ -band is designed, simulated and fabricated for verification. Results of the proposed method, simulation software and measurement are in good agreement.
Efficient virtual memory for big memory servers
(2013)

Our analysis shows that many "big-memory" server workloads, such as databases, in-memory caches, and graph analytics, pay a high cost for page-based virtual memory. They consume as much as 10% of execution cycles on TLB misses, even using large pages. On the other hand, we find that these workloads use read-write permission on most pages, are provisioned not to swap, and rarely benefit from the full flexibility of page-based virtual memory.
 To remove the TLB miss overhead for big-memory workloads, we propose mapping part of a process's linear virtual address space with a direct segment, while page mapping the rest of the virtual address space. Direct segments use minimal hardware---base, limit and offset registers per core---to map contiguous virtual memory regions directly to contiguous physical memory. They eliminate the possibility of TLB misses for key data structures such as database buffer pools and in-memory key-value stores. Memory mapped by a direct segment may be converted back to paging when needed.
 We prototype direct-segment software support for x86-64 in Linux and emulate direct-segment hardware. For our workloads, direct segments eliminate almost all TLB misses and reduce the execution time wasted on TLB misses to less than 0.5%.
Development and characterization of new generation panel fan-out (P-FO) packaging technology
(2014)

Wafer Level Packaging (WLP) is a packaging technology focusing on integrated circuit (IC) packaging at wafer level instead of die level. WLP essentially consists of IC foundry fabrication process and subsequent device interconnection and back-end passivation process. The general wafer level packages (WLPs) are designed for fan-in chip scale packaging but the shrinkage of pad pitch and size at the chip to package interface is much faster than the shrinkage at the package to board interface. The embedded package technologies are developed to provide larger package size in order to offer a sufficient area to accommodate the redistribution interconnection with standard pitches at the packaging. Embedded technology (FO-WLP / eWLB and embedded die) are unique technological breakthroughs enabling further growth of next-generation packaging modules and brings several benefits such as small form factor of packages, electrical and thermal performance improvement and cost reduction for IDMs. Compare to FO-WLP, panel level packaging (PLP) has the advantage of using large panel processing compare with FO-WLP while 12" is commonly used. Hence panel fan-out package (P-FO) has a better cost down potential than FO-WLP. To demonstrate the new generation panel fan-out package conception, the several hetero-technologies are integrated into the packaging which composed of PCB, LCD, Bumping and FOWLP has substantially broadened the spectrum of the well established wafer level packaging (WLP) technologies. The new generation panel fan-out packaging combines the different infrastructures from several fields to realize the conception. The achieved results for the package, such as the large panel size process and precise die alignment capability point to the outstanding potential of this novel hetero-system. In this paper, mixing PCB, semiconductor back-end, semiconductor WLP and LCD Gen 2.5 (370×470mm) size processing technologies combined with innovation as well as integration of P-FO techniques are proposed, including high accuracy die bonding and die shift compensation at film lamination, lower warpage sheet form film lamination, good copper trace plating uniformity control at large panel area and also precise photolithographic technique. In addition to technology elaboration and process depiction, relevant experimental data and final reliability test results on board level have also been thoroughly demonstrated and discussed.
Systems Competition and Network Effects
(1994)

Many products have little or no value in isolation, but generate value when combined with others. Examples include: nuts and bolts, which together provide fastening services; home audio or video components and programming, which together provide entertainment services; automobiles, repair parts and service, which together provide transportation services; facsimile machines and their associated communications protocols, which together provide fax services; automatic teller machines and ATM cards, which together provide transaction services; camera bodies and lenses, which together provide photographic services. These are all examples of products that are strongly complementary, although they need not be consumed in fixed proportions. We describe them as forming systems, which refers to collections of two or more components together with an interface that allows the components to work together. This paper and the others in this symposium explore the economics of such systems. Market competition between systems, as opposed to market competition between individual products, highlights at least three important issues: expectations, coordination, and compatibility. A recent wave of research has focused on the behavior and performance of the variety of private and public institutions that arise in systems markets to influence expectations, facilitate coordination, and achieve compatibility. In many cases, the components purchased for a single system are spread over time, which means that rational buyers must form expectations about
Mining knowledge-sharing sites for viral marketing
(2002)

Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.
Community structure in social and biological networks.
(2002)

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
Continuous-Scale Kinetic Fluid Simulation
(2018)

Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving the incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility. In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications. To achieve more accurate and stable simulations, we propose to employ a novel non-orthogonal central-moment-relaxation model, where we propose an adaptive relaxation method to retain both stability and accuracy in turbulent flows. To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale. Such a capability directly leads to an automatic sample construction algorithm which generates static and dynamic scales at initialization and during simulation, respectively. This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries. Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.
A proactive approach based on online reliability prediction for adaptation of service-oriented systems
(2018)

Service computing is an emerging technology in System of Systems Engineering (SoS Engineering or SoSE), which regards System as a Service (i.e. SaaS), and aims to construct a robust and value-added complex system by outsourcing external component systems through service composition technology. A service-oriented SoS runs under a dynamic and uncertain environment. To successfully deploy SoS’s run-time quality assurance, online reliability time series prediction, which aims to predict the reliability in near future for a service-oriented SoS arises as a grand challenge in SoS research. In this paper, we tackle the prediction challenge by exploiting two novel prediction models. We adopt motifs-based Dynamic Bayesian Networks (or m DBNs) model to perform one-step-ahead time series prediction, and propose a multi-steps trajectories DBNs (or multi DBNs) to further revise the future reliability prediction. Finally, an proactive adaption strategy is achieved based on the reliability prediction results. Extensive experiments conducted on real-world Web services demonstrate that our models outperform other well-known approaches consistently.
A line in the sand: a wireless sensor network for target detection, classification, and tracking
(2004)

Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called “A Line in the Sand” and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensors nodes at MacDill Air Force Base in Tampa, Florida, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.
Multi-factor Authentication Framework for Cloud Computing
(2013)

Cloud computing is a new paradigm to deliver services over the Internet. Data Security is the most critical issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Traditional password authentication does not provide enough security for information in cloud computing environment to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. The proposed framework provides a feasible and a most efficient mechanism which can closely integrate with the traditional authentication system.The proposed framework is verified by developing Cloud Access Management (CAM) system which authenticates the user based on multiple factors. Also using secret-splitting and encrypted value of arithmetic captcha is innovative factor for user authentication for cloud computing environment. Prototype model for cloud computing own cloud server is implemented using open sources technology. The proposed framework shows the close agreement with the standard criteria for security.